You are here:
Improving surface PM2.5 forecasts in the U.S. using an ensemble of chemical transport model outputs, multi satellite-based AOD products, and surface observations for AMS 101st Annual Meeting
Citation:
Zhang, H., J. Wang, L. Castro García, C. Ge, T. Plessel, J. Szykman, R. Levy, B. Murphy, AND T. Spero. Improving surface PM2.5 forecasts in the U.S. using an ensemble of chemical transport model outputs, multi satellite-based AOD products, and surface observations for AMS 101st Annual Meeting. American Meteorological Society 101st Annual Meeting, Virutal, Virtual, January 10 - 12, 2021.
Impact/Purpose:
Air quality forecasting plays an important role in informing the general public and decision-makers on reducing exposure to air pollution. Air quality models simulating atmospheric constituents such as particulate matter with a diameter less than 2.5 µm (PM2.5) are often used to provide daily forecasts. However, these models are subject to errors and uncertainty as a result of the simplified representation of the real atmosphere. Here, we develop a computationally efficient framework to improve model forecasts by performing bias correction on model outputs and use of a multi satellite-based AOD (MODIS Terra, Aqua and VIIRS) technique to fill in spatial gaps to improve surface PM2.5 forecasts in rural areas where ground observations are not available in the proximity (> 125 km).
Description:
24-hour air quality forecasts are now made in real-time in the U.S. from several chemical transport models (CTMs) that have significant differences in chemistry, emission, and meteorology. To end users at the local or regional air quality management agencies, the differences among these forecasts sometimes can lead to confusion. This work includes a two-part study that aims to develop a computationally efficient bias-correction framework to yield a single best surface PM2.5 forecast output in the U.S. through the integration of multi CTM outputs, multi satellite-based aerosol optical depth (AOD) products and surface observations. In the first part, we developed a multi-model ensemble approach together with a Kalman filter (KF) technique and a Successive Correction Method (SCM) to improve model forecasts in the non-rural areas with approximately 500 surface observation sites for PM2.5, which was applied to three (GEOS-Chem, WRF-Chem and WRF-CMAQ) CTM hindcast outputs for June 2012. While all CTMs underestimate daily surface PM2.5 mass concentration by 20-50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: 1) the arithmetic mean ensemble (AME) that equally weights each model and 2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least-square errors. The OPE shows superior performance than the AME and overall, the combination of a KF with the OPE shows the best results. Lastly, the SCM was applied to spread the bias correction from model grids with surface PM2.5 observations to the grids lacking ground observations by using a radius of influence of 125 km which further improves the forecast of surface PM2.5 at the national scale. In the second part of this work, we focus on the use of a multi satellite-based AOD (MODIS Terra, Aqua and VIIRS) technique to fill in spatial gaps to improve surface PM2.5 forecasts in rural areas where ground observations are not available in the proximity (> 125 km). This two-part study will therefore provide a synergy to combine multi-satellite observations, surface observations and multi-model outputs for improving surface PM2.5 forecasts in the U.S. and can potentially benefit local or regional air quality agencies.
